Micromechanics-based deep learning for composites

Speaker:

Assistant Prof. Mohsen Mirkhalaf

Abstract:

During the last few decades, industries such as aerospace and wind energy (among others) have been remarkably influenced by the introduction of high-performance composites.

One challenge, however, for modeling and designing composites is the lack of computational efficiency of accurate high-fidelity models. For design purposes, using conventional optimization approaches typically results in cumbersome procedures due to huge dimensions of the design space and high computational expense of full-field simulations.

In recent years, deep learning techniques have been found to be promising methods to increase the efficiency and robustness of a variety of algorithms in multi-scale modeling and design of composites.

In this presentation, I will talk about our recent activities in usage of artificial neural networks and micromechanical simulations to develop significantly fast and highly accurate models for composite materials.